Featured Blog Posts – August 2017 Archive (112)

Design Thinking: Future-proof Yourself from AI

It’s all over for us humans. It may not have been “The Matrix”[1], but the machines look like they are finally poised to take our jobs. Machines powered by artificial intelligence and machine learning process data faster, aren’t hindered by stupid human biases, don’t waste time with gossip on social media and don’t demand raises or more days…


Added by Bill Schmarzo on August 31, 2017 at 1:00pm — No Comments

Using Bayesian Kalman Filter to predict positions of moving particles / objects in 2D (in R)

In this article, we shall see how the Bayesian Kalman Filter can be used to predict positions of some moving particles / objects in 2D.

This article is inspired by a programming assignment from the coursera course Robotics Learning by University of Pennsylvania, where the goal was to implement a Kalman filter for ball tracking in 2D space. Some part of the problem description is taken from the assignment description.

The following…


Added by Sandipan Dey on August 31, 2017 at 12:30pm — No Comments

How can R Users Learn Python for Data Science ?

This article was written by Manish Saraswat.

Source for picture: …


Added by Amelia Matteson on August 30, 2017 at 6:00pm — No Comments

Python Overtakes R for Data Science and Machine Learning

This article summarizes a trend in programming languages usage, based on a number of proxy metrics. This change started to be more pronounced in early 2017: Python became the language of choice, over R, for data science and machine learning applications. 

Statistics from Google

Google has one app called Google Trend to find out trends about specific subjects, to compare interest for a number of…


Added by Vincent Granville on August 30, 2017 at 10:00am — 5 Comments

Data Science Simplified Part 9: Interactions and Limitations of Regression Models

In the last few blog posts of this series discussed regression models at length. Fernando has built a multivariate regression model. The model takes the following shape:

price = -55089.98 + 87.34 engineSize +…

Added by Pradeep Menon on August 30, 2017 at 4:30am — No Comments

Data and Analytics; Don’t Trust Numbers Blindly

Data & Analytics have become main-stream. Executives and their boards are increasingly starting to question whether their organizations are truly realizing the full value of the insights. A study suggests that 58% of organizations have difficulties evaluating the quality of the data and its reliability, raising a big question to the stakeholders as to “can you trust your data?” On one hand these is this set of people who are worried about the authenticity…


Added by Chirag Shivalker on August 30, 2017 at 3:30am — No Comments

Machine Learning in Fintech - Demystified

Abstract – Big data helps to make strategy for future and understand user behaviors. In 1959, Arther Samuel gave very simple definition of Machine Learning as “a Field of study that gives computer the ability to learn without being explicitly programmed”. Now almost after 58 years from then we still have not progressed much beyond this definition if we compare the progress we made in other areas from same time. The idea of FinTech adopting some best practices from the Big…


Added by Vinod Sharma on August 29, 2017 at 11:00pm — No Comments

AI as a Catalyst Across Most Cycles of the IoT

This article was written by Roger Strukhoff and Sophie Turol.

A scary rate of…


Added by Amelia Matteson on August 29, 2017 at 3:00pm — No Comments

Comprehensive Repository of Data Science and ML Resources

Here a list of resources, mostly in the form of tutorials, covering most important topics in data science: This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving…


Added by Vincent Granville on August 29, 2017 at 2:30pm — 2 Comments

Reinforcement Learning Part 3 – Challenges & Considerations

Summary:  In the first part of this series we described the basics of Reinforcement Learning (RL).  In this article we describe how deep learning is augmenting RL and a variety of challenges and considerations that need to be addressed in each implementation.


In the first part of this series,…


Added by William Vorhies on August 29, 2017 at 9:03am — No Comments

Cross-Validation: Concept and Example in R

This article was written by Sondos Atwi.

What is Cross-Validation?

In Machine Learning, Cross-validation is a resampling method used for model evaluation to avoid testing a model on the same dataset on which it was trained. This is a common mistake, especially that a separate testing dataset is not always available. However, this usually leads to inaccurate…


Added by Amelia Matteson on August 28, 2017 at 7:00pm — No Comments

Einstein Platform: Fuel for AI-enabled World

Brief Overview of  SalesforceEinstein — APIs that allow building AI-powered apps fast.

Recently, Salesforce — one of the world’s progressive CRM platforms introduced a new set of powerful APIs responding to data science communities around the…


Added by Max Frolov on August 28, 2017 at 8:00am — No Comments

Why Cognitive Systems should combine Machine Learning with Semantic Technologies

Will Artificial Intelligence make subject matter experts obsolete?

Imagine you want to build an application that helps to identify wine and cheese pairings. Who will perform best? Applications solely based on machine learning, those ones which are based on experts' knowledge only, or a combination of both?

Most of the machine learning algorithms were…


Added by Andreas Blumauer on August 28, 2017 at 3:00am — 1 Comment

A Standard to build Knowledge Graphs: 12 Facts about SKOS

These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time.



Added by Andreas Blumauer on August 28, 2017 at 3:00am — No Comments

Why Small Business Needs Big Data?

Small business enterprises are being said to be the biggest game changer in the current global business scenario. Needless to say, big data and data analysis tools are definitely going to play a vital role in this context, while a variety of data analysis software solutions is all ready to power this revolution.


How Big Data Is Going To Transform Your Business?

Every single human activity taking place across the globe generates data creating an extremely…


Added by Shantanu Chaturvedi on August 28, 2017 at 2:30am — No Comments

Judgements: A Potential Threat To Models Everywhere

Data Science and Law are in a lot of ways vastly different; however they do have a few things in common: both professions rely heavily on historical data and patterns . Certain supervening phenomena can cause a break in these patterns; these distinct deviations from a learned practice (either by a machine learning algorithm or through pure human experience) can be problematic.

Those of us in Legal Modelling know that when building and maintaining models we are constantly looking…


Added by Mkhuseli Mthukwane on August 28, 2017 at 2:30am — 1 Comment

The Triple-layered Reporting Architecture

By JIANG Buxing 

In conventional reporting architecture, a reporting tool is connected directly to data sources, without a data computing layer in between. Most of the time, the middle layer isn’t needed, and the computing purpose can be realized within the data source and by the reporting tool respectively. But development experience has taught us that there are certain types of reports for which the computations are not suitable to be handled either by data source or the…


Added by JIANG Buxing on August 27, 2017 at 10:30pm — 3 Comments

Deep Learning Explained - in 4 Simple Facts

This article comes from a writer at Steemit.

First off, let me say: this topic is vast. In my article, I’ll try to boil down the main facts, but be warned, you should investigate the matter on your own to learn more.

Hope I can set you on the right path at least. Lets dive in!…


Added by Amelia Matteson on August 27, 2017 at 4:30pm — No Comments

Weekly Digest, August 28

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week.


  • JPMorgan Chase is looking for software engineers with expertise in machine learning and natural language processing to help us build the solutions of the future. We apply advanced ML…

Added by Vincent Granville on August 27, 2017 at 8:30am — No Comments

Machine Learning - The brain of Digital Transformation

We are all familiar with machine learning in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better shopping and movie experience.

Artificial intelligence (AI) has stormed the world today. It is an umbrella term that includes multiple technologies, such as machine learning, deep learning, and…


Added by Sandeep Raut on August 27, 2017 at 3:00am — No Comments

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